ExpHunterSuite: Differential Expression Report
Data quality control (QC)
Correlation between samples:
Here we show scatterplots comparing expression levels for all genes between the different samples, for i) all controls, ii) all treatment samples and iii) for all samples together.
These plots will only be produced when the total number of samples to compare within a group is less than or equal to 10.
Heatmap and clustering showing correlation between replicates
BROWN: higher correlation; YELLOW: lower
Principal Component Analysis
This is a PCA plot of the count values normalized following the default method and then they are scaled:
Inertia plot exploring variance explained by dimensions
Graphical representation of PCA dimensions. The bars represent the percentage of total variance that summarize each dimension. The line measures the percentage of total variance accumulated in previous dimensions. The color distinguishes between significan or no significant dimensions. Only significant dimensions will be considered in the following plots.
Distribution of Eigenvectors
The eigenvector contains the weights of each gene for the PC. Here are represented the distributions of the weights of each eigenvector. The vertical lines represent the quantiles.
Comparison of significant dimensions significant dimensions
This plot compare the position of samples and their distribution in the significant dimensions. The color differenciate between the control (red) and treat (blue) samples.
Representation of the samples in the two first dimension of PCA
Representation of the samples and the categories of qualitative valiables in the two first dimension of PCA
Representation of the variable contribution to the PCA axis
1 and
2
Representation of the variable contribution to the PCA axis
3 and
4
Representation of the individuals and the variable
contribution to the PCA axis 1 and
2
Hierarchical clustering of individuals using first 20
significant PCA
dimensions
PCA representation of 1 and 2 axis with individuals coloured
by its cluster membership. The first 20 significant PCA dimensions are
used for
HCPC
Relationship between HCPC clusters and experiment design
Fisher’s exact test is computed between clusters and experimental treats. Fisher’s exact test P values and FDR are showed.
Representation of correlation and P value of numeric factors and PCA dimensions
Representation of R2 and P value of qualitative factors and PCA dimensions
Representation of estimated coordinated from barycentre and P value of qualitative factors and PCA dimensions
Visualizing normalization results
These boxplots show the distributions of count data before and after normalization (shown for normalization method default):
Representation of cpm unfiltered data:
Before normalization:
After normalization:
Count metrics by sample ranks
Sample rank versus total counts
Sample rank is the position a sample holds after sorting by total counts
Statistics of expressed genes
Samples are ranked by total expressed genes. Union of expressed genes represents the cumulative total expressed genes (sum of all genes expressed in any sample up to current sample, expected to increase with sample rank). Intersection of expressed genes represents the cumulative intersection of expressed genes (sum of genes expressed in all samples up to current sample, expected to decrease with sample rank).
Mean count distribution by filter
This plot represents the mean counts distribution per gene, classified by filters
Gene counts variance distribution
Variance of gene counts across samples are represented. Genes with lower variance than selected threshold (dashed grey line) were filtered out.
Samples differences by all counts normalized:
All counts were normalizated by default (see options below) algorithm. This count were scaled by log10 and plotted in a heatmap.
Percentages of reads per sample mapping to the most highly expressed genes
| M_19254 | M_19256 | M_19257 | M_20268 | M_20282 | M_20283 | M_20284 | M_20281 | M_20279 | M_20262 | M_20280 | M_20270 | M_20272 | M_20273 | M_20296 | M_20289 | M_20275 | M_20276 | M_20301 | M_20293 | M_20286 | M_20292 | M_20290 | M_20291 | M_20294 | M_20295 | M_20298 | M_20299 | M_20302 | M_20303 | M_20304 | M_20312 | M_20307 | M_20308 | M_20309 | M_20310 | M_20311 | M_20319 | M_20318 | M_20328 | M_20313 | M_20315 | M_20316 | M_20317 | M_20322 | M_20327 | M_20325 | M_20326 | M_20324 | M_20329 | M_20331 | M_1273 | M_1280 | M_1284 | M_1285 | M_1286 | M_1287 | M_1290 | M_1292 | M_1274 | M_1295 | M_1296 | M_1298 | M_1299 | M_12150 | M_12151 | M_12152 | M_12153 | M_12154 | M_12155 | M_12156 | M_13158 | M_13160 | M_13162 | M_13163 | M_13164 | M_13165 | M_13168 | M_16173 | M_16184 | M_16170 | M_16172 | M_16181 | M_16182 | M_16171 | M_16174 | M_16175 | M_16180 | M_16178 | M_19255 | M_16186 | M_16176 | M_16177 | M_16188 | M_16189 | M_16179 | M_16187 | M_16195 | M_16183 | M_16185 | M_16196 | M_16200 | M_16190 | M_16191 | M_16194 | M_16192 | M_17204 | M_16197 | M_16198 | M_16199 | M_16201 | M_17203 | M_17208 | M_17214 | M_17205 | M_17206 | M_17209 | M_17207 | M_17210 | M_17211 | M_17212 | M_17216 | M_17213 | M_17215 | M_19223 | M_19218 | M_19219 | M_19220 | M_19222 | M_19229 | M_19221 | M_19245 | M_19230 | M_19224 | M_19232 | M_19227 | M_19228 | M_19233 | M_19234 | M_19240 | M_19261 | M_19235 | M_19249 | M_19248 | M_19236 | M_19238 | M_19251 | M_19252 | M_19237 | M_19253 | M_19246 | M_19247 | M_20267 | M_20265 | M_20264 | M_19260 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000274012.1 | 6.878 | 3.932 | 3.825 | 3.848 | 6.135 | 5.342 | 6.342 | 5.491 | 5.454 | 10.132 | 5.588 | 6.666 | 5.520 | 7.013 | 9.263 | 5.777 | 10.510 | 7.662 | 10.495 | 7.571 | 6.800 | 6.321 | 6.408 | 7.025 | 6.735 | 6.834 | 8.957 | 9.044 | 5.904 | 7.805 | 10.442 | 7.127 | 8.814 | 6.953 | 8.204 | 7.097 | 5.204 | 5.650 | 5.739 | 7.633 | 7.085 | 6.187 | 5.533 | 4.761 | 6.765 | 6.619 | 6.928 | 8.186 | 5.581 | 7.871 | 7.720 | 6.752 | 7.150 | 8.169 | 5.610 | 7.955 | 8.417 | 6.233 | 9.142 | 7.884 | 8.840 | 5.824 | 4.374 | 7.298 | 4.741 | 6.361 | 4.784 | 6.805 | 5.405 | 6.073 | 5.776 | 8.347 | 6.818 | 9.328 | 7.851 | 8.075 | 8.308 | 5.963 | 6.097 | 7.206 | 5.416 | 7.223 | 7.745 | 7.439 | 7.411 | 7.229 | 7.043 | 8.241 | 7.928 | 7.720 | 6.924 | 7.833 | 7.805 | 4.415 | 10.227 | 7.705 | 6.728 | 9.119 | 9.155 | 8.705 | 7.370 | 7.204 | 6.911 | 7.317 | 9.526 | 5.577 | 5.269 | 13.838 | 6.578 | 7.102 | 6.627 | 5.402 | 9.061 | 7.999 | 5.839 | 7.713 | 7.801 | 9.221 | 8.846 | 9.086 | 9.556 | 6.125 | 8.132 | 5.850 | 11.158 | 7.597 | 8.778 | 7.478 | 6.224 | 5.359 | 9.484 | 5.315 | 6.034 | 7.023 | 6.911 | 8.029 | 7.167 | 6.441 | 7.263 | 6.567 | 7.213 | 4.749 | 5.652 | 5.448 | 5.434 | 7.643 | 7.475 | 5.551 | 5.345 | 4.849 | 6.317 | 6.183 | 7.675 | 7.224 | 9.346 | 8.154 |
| ENSG00000276168.1 | 4.705 | 3.269 | 3.573 | 4.508 | 5.140 | 4.848 | 5.758 | 4.871 | 5.031 | 8.814 | 5.582 | 6.931 | 6.090 | 6.854 | 7.929 | 5.754 | 9.958 | 6.841 | 8.771 | 7.399 | 5.271 | 6.161 | 5.792 | 6.286 | 6.728 | 6.343 | 8.351 | 6.929 | 5.700 | 7.154 | 9.599 | 6.025 | 7.145 | 6.022 | 6.173 | 5.418 | 4.210 | 5.384 | 4.382 | 6.510 | 5.789 | 4.217 | 3.270 | 3.222 | 5.121 | 5.695 | 6.649 | 5.875 | 3.863 | 7.584 | 6.161 | 6.177 | 5.559 | 3.616 | 5.329 | 6.291 | 6.929 | 4.901 | 5.901 | 6.578 | 7.230 | 5.351 | 4.598 | 6.793 | 4.502 | 5.351 | 4.620 | 6.470 | 4.086 | 4.740 | 5.234 | 7.482 | 6.668 | 8.155 | 5.016 | 6.437 | 7.007 | 4.707 | 4.231 | 5.760 | 4.680 | 4.922 | 6.865 | 4.754 | 5.780 | 6.372 | 4.947 | 5.767 | 6.378 | 5.786 | 6.819 | 6.110 | 7.035 | 5.014 | 8.076 | 6.383 | 5.657 | 7.002 | 6.713 | 8.074 | 6.772 | 6.974 | 5.265 | 5.949 | 6.396 | 3.781 | 4.733 | 12.153 | 5.037 | 5.671 | 2.716 | 5.000 | 8.082 | 6.380 | 4.594 | 6.253 | 7.075 | 7.963 | 7.918 | 6.419 | 7.978 | 5.073 | 7.426 | 4.473 | 9.653 | 6.206 | 7.504 | 6.426 | 4.999 | 4.705 | 7.318 | 4.692 | 5.063 | 5.245 | 4.469 | 7.052 | 5.568 | 4.720 | 5.807 | 5.194 | 4.476 | 4.129 | 5.356 | 4.329 | 4.005 | 6.450 | 4.012 | 4.508 | 5.153 | 4.204 | 4.555 | 5.314 | 6.045 | 5.140 | 7.586 | 6.548 |
| ENSG00000283293.1 | 1.776 | 0.969 | 1.057 | 1.533 | 1.656 | 1.911 | 1.781 | 1.888 | 2.166 | 2.201 | 1.871 | 2.069 | 1.792 | 2.355 | 2.176 | 2.137 | 1.628 | 1.946 | 2.239 | 1.697 | 2.053 | 1.923 | 2.152 | 2.438 | 1.885 | 2.130 | 2.082 | 2.610 | 1.833 | 2.051 | 2.434 | 1.476 | 2.743 | 1.367 | 2.047 | 1.838 | 1.423 | 1.835 | 1.692 | 2.186 | 1.480 | 1.569 | 2.630 | 2.157 | 1.768 | 1.724 | 1.791 | 1.769 | 1.787 | 2.027 | 2.265 | 1.819 | 2.264 | 1.835 | 1.904 | 2.073 | 1.912 | 1.837 | 2.327 | 1.742 | 2.019 | 1.702 | 1.771 | 2.067 | 1.642 | 1.858 | 1.444 | 1.454 | 1.250 | 1.949 | 1.831 | 2.252 | 1.838 | 1.947 | 2.430 | 1.975 | 2.104 | 1.566 | 2.202 | 1.845 | 1.591 | 2.083 | 2.021 | 2.307 | 1.685 | 1.917 | 1.401 | 2.239 | 2.043 | 1.553 | 1.791 | 1.940 | 2.158 | 1.357 | 2.362 | 1.949 | 1.554 | 1.684 | 2.490 | 1.966 | 1.779 | 2.242 | 1.768 | 1.393 | 2.039 | 1.742 | 1.150 | 2.123 | 1.639 | 1.307 | 1.680 | 1.131 | 2.327 | 1.921 | 1.395 | 1.908 | 1.775 | 1.928 | 2.105 | 2.221 | 2.136 | 1.565 | 2.076 | 1.722 | 2.626 | 2.214 | 2.354 | 1.612 | 1.494 | 1.172 | 2.588 | 1.351 | 1.568 | 2.233 | 1.764 | 2.058 | 1.700 | 1.347 | 1.275 | 1.644 | 1.707 | 1.304 | 1.313 | 1.399 | 1.661 | 2.194 | 1.321 | 1.406 | 1.364 | 1.134 | 1.408 | 1.424 | 1.748 | 1.544 | 1.894 | 1.730 |
| ENSG00000251562.9 | 1.656 | 1.311 | 1.474 | 0.946 | 1.224 | 1.150 | 1.684 | 1.362 | 1.170 | 1.041 | 1.227 | 1.051 | 1.121 | 1.271 | 1.202 | 0.976 | 1.069 | 1.412 | 1.180 | 1.221 | 1.297 | 1.351 | 1.018 | 1.153 | 1.509 | 1.258 | 1.066 | 1.218 | 1.504 | 1.247 | 0.817 | 1.337 | 1.099 | 1.403 | 1.223 | 1.794 | 1.891 | 1.496 | 1.574 | 1.482 | 1.583 | 1.422 | 1.005 | 1.330 | 1.457 | 1.239 | 1.464 | 1.653 | 1.666 | 1.245 | 1.163 | 1.565 | 1.407 | 1.421 | 1.554 | 1.433 | 1.454 | 1.381 | 1.284 | 1.290 | 1.259 | 1.287 | 0.389 | 1.454 | 1.290 | 1.289 | 1.699 | 1.580 | 1.564 | 1.171 | 1.654 | 1.188 | 1.173 | 1.279 | 1.335 | 1.212 | 1.238 | 1.556 | 1.695 | 1.216 | 1.626 | 1.656 | 1.213 | 1.632 | 1.368 | 1.382 | 1.591 | 1.203 | 1.265 | 1.428 | 1.170 | 1.414 | 1.125 | 1.567 | 1.155 | 1.280 | 1.590 | 1.226 | 1.170 | 0.980 | 1.336 | 1.037 | 1.189 | 1.475 | 1.066 | 1.772 | 1.513 | 0.091 | 1.732 | 0.883 | 1.336 | 1.095 | 1.061 | 1.263 | 1.273 | 1.158 | 1.285 | 1.256 | 1.210 | 1.264 | 1.182 | 1.667 | 1.107 | 1.479 | 0.854 | 1.248 | 0.965 | 1.225 | 1.424 | 1.373 | 1.252 | 1.277 | 1.422 | 1.810 | 1.213 | 1.247 | 1.651 | 1.318 | 1.149 | 1.144 | 1.676 | 1.123 | 1.550 | 1.306 | 1.487 | 1.316 | 0.844 | 1.490 | 1.150 | 1.349 | 1.241 | 1.223 | 1.255 | 1.615 | 1.167 | 1.242 |
| ENSG00000124942.14 | 1.066 | 1.622 | 1.310 | 0.788 | 1.058 | 0.968 | 1.091 | 1.273 | 1.279 | 1.110 | 1.024 | 0.912 | 1.049 | 1.014 | 1.176 | 1.047 | 0.911 | 1.200 | 1.130 | 0.921 | 1.014 | 1.080 | 0.956 | 1.088 | 1.110 | 1.182 | 0.981 | 0.889 | 1.047 | 1.092 | 0.803 | 1.030 | 0.870 | 1.051 | 1.274 | 1.374 | 1.533 | 1.211 | 1.171 | 1.117 | 1.138 | 1.238 | 0.961 | 1.330 | 1.137 | 1.159 | 1.171 | 1.222 | 1.362 | 0.898 | 1.286 | 1.015 | 0.972 | 1.365 | 1.261 | 1.070 | 0.950 | 1.025 | 1.179 | 0.975 | 1.093 | 1.030 | 0.928 | 1.011 | 1.150 | 1.183 | 1.153 | 1.246 | 1.332 | 1.428 | 1.246 | 0.889 | 1.049 | 0.964 | 0.888 | 1.293 | 1.106 | 1.316 | 0.928 | 1.110 | 1.329 | 1.313 | 1.203 | 0.943 | 1.423 | 1.347 | 1.191 | 1.356 | 1.169 | 1.308 | 1.030 | 1.506 | 1.093 | 1.008 | 0.963 | 1.169 | 1.200 | 1.297 | 1.156 | 1.116 | 1.284 | 1.262 | 1.624 | 1.134 | 1.200 | 1.167 | 1.123 | 0.443 | 1.083 | 1.212 | 1.384 | 1.473 | 1.080 | 1.029 | 1.390 | 1.205 | 0.960 | 0.895 | 0.875 | 1.009 | 1.162 | 1.424 | 1.095 | 1.197 | 1.027 | 1.297 | 1.184 | 1.185 | 1.126 | 1.285 | 0.819 | 1.393 | 1.411 | 1.317 | 1.586 | 1.327 | 1.070 | 1.581 | 1.193 | 1.501 | 1.490 | 1.208 | 1.216 | 1.492 | 1.198 | 1.009 | 1.093 | 1.311 | 0.998 | 1.345 | 1.248 | 1.138 | 1.125 | 1.313 | 0.923 | 1.223 |
Details of the input data
First group of samples (to be referred to as control in the rest of the report)
| Sample Names: |
|---|
| M_19254 |
| M_19256 |
| M_19257 |
| M_20268 |
| M_20282 |
| M_20283 |
| M_20284 |
| M_20281 |
| M_20279 |
| M_20262 |
| M_20280 |
| M_20270 |
| M_20272 |
| M_20273 |
| M_20296 |
| M_20289 |
| M_20275 |
| M_20276 |
| M_20301 |
| M_20293 |
| M_20286 |
| M_20292 |
| M_20290 |
| M_20291 |
| M_20294 |
| M_20295 |
| M_20298 |
| M_20299 |
| M_20302 |
| M_20303 |
| M_20304 |
| M_20312 |
| M_20307 |
| M_20308 |
| M_20309 |
| M_20310 |
| M_20311 |
| M_20319 |
| M_20318 |
| M_20328 |
| M_20313 |
| M_20315 |
| M_20316 |
| M_20317 |
| M_20322 |
| M_20327 |
| M_20325 |
| M_20326 |
| M_20324 |
| M_20329 |
| M_20331 |
| M_1273 |
| M_1280 |
| M_1284 |
| M_1285 |
| M_1286 |
| M_1287 |
| M_1290 |
| M_1292 |
| M_1274 |
| M_1295 |
| M_1296 |
| M_1298 |
| M_1299 |
| M_12150 |
| M_12151 |
| M_12152 |
| M_12153 |
| M_12154 |
| M_12155 |
| M_12156 |
| M_13158 |
| M_13160 |
| M_13162 |
| M_13163 |
| M_13164 |
| M_13165 |
| M_13168 |
Second group of samples (to be referred to as treatment in the rest of the report)
| Sample Names: |
|---|
| M_16173 |
| M_16184 |
| M_16170 |
| M_16172 |
| M_16181 |
| M_16182 |
| M_16171 |
| M_16174 |
| M_16175 |
| M_16180 |
| M_16178 |
| M_19255 |
| M_16186 |
| M_16176 |
| M_16177 |
| M_16188 |
| M_16189 |
| M_16179 |
| M_16187 |
| M_16195 |
| M_16183 |
| M_16185 |
| M_16196 |
| M_16200 |
| M_16190 |
| M_16191 |
| M_16194 |
| M_16192 |
| M_17204 |
| M_16197 |
| M_16198 |
| M_16199 |
| M_16201 |
| M_17203 |
| M_17208 |
| M_17214 |
| M_17205 |
| M_17206 |
| M_17209 |
| M_17207 |
| M_17210 |
| M_17211 |
| M_17212 |
| M_17216 |
| M_17213 |
| M_17215 |
| M_19223 |
| M_19218 |
| M_19219 |
| M_19220 |
| M_19222 |
| M_19229 |
| M_19221 |
| M_19245 |
| M_19230 |
| M_19224 |
| M_19232 |
| M_19227 |
| M_19228 |
| M_19233 |
| M_19234 |
| M_19240 |
| M_19261 |
| M_19235 |
| M_19249 |
| M_19248 |
| M_19236 |
| M_19238 |
| M_19251 |
| M_19252 |
| M_19237 |
| M_19253 |
| M_19246 |
| M_19247 |
| M_20267 |
| M_20265 |
| M_20264 |
| M_19260 |
Note: A positive log fold change shows higher expression in the treatment group; a negative log fold change represents higher expression in the control group.
DEgenes Hunter results
Gene classification by DEgenes Hunter
DEgenes Hunter uses multiple DE detection packages to analyse all genes in the input count table and labels them accordingly:
- Filtered out: Genes discarded during the filtering process as showing no or very low expression.
- Prevalent DEG: Genes considered as differentially
expressed (DE) by at least 4 packages, as specified by the
minpack_commonargument. - Possible DEG: Genes considered DE by at least one of the DE detection packages.
- Not DEG: Genes not considered DE in any package.
This barplot shows the total number of genes passing each stage of analysis - from the total number of genes in the input table of counts, to the genes surviving the expression filter, to the genes detected as DE by one package, to the genes detected by at least 4 packages.
Package DEG detection stats
This is the Venn Diagram of all possible DE genes (DEGs) according to at least on of the DE detection packages employed:
FDR gene-wise benchmarking
Benchmark of false positive calling:
Boxplot of FDR values among all genes with an FDR <= 0.05 in at least one DE detection package
FDR Volcano Plot showing log 2 fold change vs. FDR
The red horizontal line represents the chosen FDR threshold of 0.05. The black lines represent other values.
Principal Component Analysis
This is a PCA plot of the count values normalized following the default method and then they are scaled:
Inertia plot exploring variance explained by dimensions
Graphical representation of PCA dimensions. The bars represent the percentage of total variance that summarize each dimension. The line measures the percentage of total variance accumulated in previous dimensions. The color distinguishes between significan or no significant dimensions. Only significant dimensions will be considered in the following plots.
Distribution of Eigenvectors
The eigenvector contains the weights of each gene for the PC. Here are represented the distributions of the weights of each eigenvector. The vertical lines represent the quantiles.
Comparison of significant dimensions significant dimensions
This plot compare the position of samples and their distribution in the significant dimensions. The color differenciate between the control (red) and treat (blue) samples.
Representation of the samples in the two first dimension of PCA
Representation of the samples and the categories of qualitative valiables in the two first dimension of PCA
Representation of the variable contribution to the PCA axis
1 and
2
Representation of the variable contribution to the PCA axis
3 and
4
Representation of the individuals and the variable
contribution to the PCA axis 1 and
2
Hierarchical clustering of individuals using first 9
significant PCA
dimensions
PCA representation of 1 and 2 axis with individuals coloured
by its cluster membership. The first 9 significant PCA dimensions are
used for
HCPC
Relationship between HCPC clusters and experiment design
Fisher’s exact test is computed between clusters and experimental treats. Fisher’s exact test P values and FDR are showed.
Representation of correlation and P value of numeric factors and PCA dimensions
Representation of R2 and P value of qualitative factors and PCA dimensions
Representation of estimated coordinated from barycentre and P value of qualitative factors and PCA dimensions
The complete results of the DEgenes Hunter differential expression analysis can be found in the “hunter_results_table.txt” file in the Common_results folder
DE detection package specific results
Various plots specific to each package are shown below:
DESeq2 size factor vs. sample rank
The effective library size is the factor used by DESeq2 normalization algorithm for each sample. The effective library size must be dependent of raw library size.
DESeq2 normalization effects:
This plot compares the effective library size with raw library size
The effective library size is the factor used by DESeq2 normalization algorithm for each sample. The effective library size must be dependent of raw library size.
DESeq2 MA plot:
This is the MA plot from DESeq2 package:
In DESeq2, the MA-plot (log ratio versus abundance) shows the log2 fold changes are attributable to a given variable over the mean of normalized counts. Points will be colored red if the adjusted Pvalue is less than 0.1. Points which fall out of the window are plotted as open triangles pointing either up or down.
A table containing the DESeq2 DEGs is provided: in Results_DESeq2/DEgenes_DESEq2.txt
A table containing the DESeq2 normalized counts is provided in Results_DESeq2/Normalized_counts_DESEq2.txt
Differences between samples by PREVALENT DEGs normalized counts:
Counts of prevalent DEGs were normalizated by DESeq2 algorithm. This count were scaled by log10 and plotted in a heatmap.
Detailed package results comparation
This is an advanced section in order to compare the output of the packages used to perform data analysis. The data shown here does not necessarilly have any biological implication.
P-value Distributions
Distributions of p-values, unadjusted and adjusted for multiple testing (FDR)
FDR Correlations
Correlations of adjusted p-values, adjusted for multiple testing (FDR) and for log Fold Change.
Values of options used to run DEGenesHunter
First column contains the option names; second column contains the given values for each option in this run.
| opt | |
|---|---|
| input_file | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/data/pseudocounts_table.txt |
| pseudocounts | TRUE |
| reads | 2 |
| count_var_quantile | 0 |
| minlibraries | 2 |
| filter_type | separate |
| output_files | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/reboot/results |
| p_val_cutoff | 0.05 |
| lfc | 1 |
| modules | D |
| minpack_common | 4 |
| target_file | /mnt/home/users/bio_267_uma/elenarojano/projects/tfms/albaSubiri/data/parse_outbrat/parsed_files/experiment_design.txt |
| model_variables | |
| numerics_as_factors | FALSE |
| string_factors | Etnia,Sex,AgeHigher50,Smoker,Hypertension,Dyslipidemia,Diabetes,Obesity,Antihypertensives_HPM,DM2,AntiDM,Quartils.Temp,Quartils.MinTemp,Quartil.MaxTemp,Healthy,FIB4.stage,FIB4.class,FIB4.Adv.Fib |
| numeric_factors | Age,Weight,Height,BMI,Waist,Hip,whr,sbp,dbp,AntiHPN_T1,AntiHPN_T2,AntiHPN_T3,AntiHPN_T4,Glucose,HbA1c,AntiDM_T1,AntiDM_T2,AntiDM_T3,AntiDM_T4,AntiDM_T5,Cholesterol,HDLc,LDLc,Triglycerides,Creatinine,GOT,GPT,ultraPCR,Ferritin,Hemoglobin,Hematocrit,Hematies,Platelets,MinTemp,MaxTemp,AverageTemp,MinTempBefore,MaxTempBefore,AverageTempBefore,MeanMinTemp,MeanMaxTemp,MeanAvgTemp,As,AsBefore,BenzoPireno,BenzoPirenoBefore,C6H6,C6H6Before,CO,CObefore,NO,NObefore,NO2,NO2before,NOX,NOXbefore,O3,O3before,PM2.5,PM2.5before,PM10,PM10before,HoresSol_Suma,HoresSol_Mitja,FIB4.score |
| WGCNA_memory | 5000 |
| WGCNA_norm_method | DESeq2 |
| WGCNA_deepsplit | 2 |
| WGCNA_min_genes_cluster | 20 |
| WGCNA_detectcutHeight | 0.995 |
| WGCNA_mergecutHeight | 0.25 |
| WGCNA_all | FALSE |
| WGCNA_blockwiseNetworkType | signed |
| WGCNA_blockwiseTOMType | signed |
| WGCNA_minCoreKME | 0.7 |
| WGCNA_minKMEtoStay | 0.5 |
| WGCNA_corType | pearson |
| multifactorial | |
| help | FALSE |